The Billion-Dollar Race to Build AI That Can Actually Reason
In September 2024, OpenAI released o1, a model that could do something its predecessors couldn't: think step by step before answering. It wasn't just pattern matching anymore: it was reasoning, or at least something that looked remarkably like it. Within months, every major AI lab was racing to build their own reasoning models. The stakes? Nothing less than the path to artificial general intelligence.
Traditional language models answer questions by predicting the most likely next word. This works amazingly well for many tasks, but it fails spectacularly when problems require multi-step logical deduction. Ask GPT-4 to solve a novel math problem, and it often stumbles. Ask it to plan a complex project with interdependencies, and it loses the thread.
Reasoning models work differently. They're trained to generate a chain of thought: breaking problems into steps, checking their own work, backtracking when they hit dead ends. The inference process is slower and more expensive, but the results on hard problems are dramatically better.
OpenAI's o-series uses reinforcement learning to train models to reason during inference. The o1 model, followed by o3, showed remarkable performance on math, coding, and science benchmarks. But the approach is computationally expensive: a single query can cost 10 to 50 times more than a standard GPT-4 response.
Google DeepMind's Gemini team has integrated reasoning into their models more seamlessly, with "thinking" modes that can be toggled on and off. Their approach aims for efficiency: getting reasoning-like performance without the massive compute overhead.
Anthropic's Claude has focused on what they call "extended thinking," allowing models to work through problems in a structured way while maintaining the ability to explain their reasoning to users.
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